OpenRaiser/Intern-Atlas huggingface dataset:
eSapiens: A Platform for Secure and Auditable Retrieval-Augmented Generation
Venue: arXiv cs.AI 2025
Year: 2025

We present eSapiens, an AI-as-a-Service (AIaaS) platform engineered around a business-oriented trifecta: proprietary data, operational workflows, and any major agnostic Large Language Model (LLM). eSapiens gives businesses full control over their AI assets, keeping everything in-house for AI knowledge retention and data security. eSapiens AI Agents (Sapiens) empower your team by providing valuable insights and automating repetitive tasks, enabling them to focus on high-impact work and drive better business outcomes. The system integrates structured document ingestion, hybrid vector retrieval, and no-code orchestration via LangChain, and supports top LLMs including OpenAI, Claude, Gemini, and DeepSeek. A key component is the THOR Agent, which handles structured SQL-style queries and generates actionable insights over enterprise databases. To evaluate the system, we conduct two experiments. First, a retrieval benchmark on legal corpora reveals that a chunk size of 512 tokens yields the highest retrieval precision (Top-3 accuracy: 91.3%). Second, a generation quality test using TRACe metrics across five LLMs shows that eSapiens delivers more context-consistent outputs with up to a 23% improvement in factual alignment. These results demonstrate the effectiveness of eSapiens in enabling trustworthy, auditable AI workflows for high-stakes domains like legal and finance.
Demystifying Flux Architecture
Venue: arXiv cs.CV 2025
Year: 2025

FLUX.1 is a diffusion-based text-to-image generation model developed by Black Forest Labs, designed to achieve faithful text-image alignment while maintaining high image quality and diversity. FLUX is considered state-of-the-art in text-to-image generation, outperforming popular models such as Midjourney, DALL-E 3, Stable Diffusion 3 (SD3), and SDXL. Although publicly available as open source, the authors have not released official technical documentation detailing the model's architecture or training setup. This report summarizes an extensive reverse-engineering effort aimed at demystifying FLUX's architecture directly from its source code, to support its adoption as a backbone for future research and development. This document is an unofficial technical report and is not published or endorsed by the original developers or their affiliated institutions.
Complexity and Coupling: A Functional Domain Approach
Venue: arXiv cs.SE 2025
Year: 2025

This paper provides a precise and scientific definition of complexity and coupling, grounded in the functional domain, particularly within industrial control and automation systems (iCAS). We highlight the widespread ambiguity in defining complexity and coupling, emphasizing that many existing definitions rooted in physical attributes lead to confusion and inconsistencies. Furthermore, we re-exhibit why coupled design inherently increases complexity and how potentially this complexity could be reduced. Drawing on examples from various disciplines, such as software engineering, industrial automation, and mechanical design, we demonstrate that complexity does not necessarily correlate with system size or the number of components, and coupling, unlike common belief in software engineering, actually does not occur in the physical domain but in the functional domain. We conclude that effective design necessitates addressing coupling and complexity within the functional domain.
Ensemble Confidence Calibration for Sound Event Detection in Open-environment
Venue: arXiv cs.SD 2025
Year: 2025

Sound event detection (SED) has made strong progress in controlled environments with clear event categories. However, real-world applications often take place in open environments. In such cases, current methods often produce predictions with too much confidence and lack proper ways to measure uncertainty. This limits their ability to adapt and perform well in new situations. To solve this problem, we are the first to use ensemble methods in SED to improve robustness against out-of-domain (OOD) inputs. We propose a confidence calibration method called Energy-based Open-World Softmax (EOW-Softmax), which helps the system better handle uncertainty in unknown scenes. We further apply EOW-Softmax to sound occurrence and overlap detection (SOD) by adjusting the prediction. In this way, the model becomes more adaptable while keeping its ability to detect overlapping events. Experiments show that our method improves performance in open environments. It reduces overconfidence and increases the ability to handle OOD situations.
Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC
Venue: arXiv cs.CR 2025
Year: 2025

The existing MPC-based private inference frameworks either rely on impractical real-world assumptions, or adopt the strongest security model (Malicious Security Dishonest Majority, MSDM) and then suffer from severe efficiency limitations. To balance security and efficiency, we propose a novel, three-layer private inference framework based on the Helper-Assisted MSDM (HA-MSDM) model. The first is the primitive layer, where we extend computations from prime fields to rings for efficient fixed-point arithmetic and then better support inference operations. The second is the MPC layer, where we design six fixed-round MPC protocols to reduce latency for core operations like multiplication, polynomial evaluation, and batch check. The third is the inference layer, which can achieve efficient and high-accuracy CNN inference. The efficiency is achieved by applying our designed MPC protocols. The high-accuracy private inference in deep CNNs is achieved by designing a co-optimized strategy, which employs high-precision polynomial approximation for activation functions and uses parameter-adjusted Batch Normalization layers to constrain inputs. Benchmarks on LeNet and AlexNet show our framework achieves up to a 2.4-25.7x speedup in LAN and a 1.3-9.5x acceleration in WAN over the state-of-the-art MSDM frameworks with only 0.04-1.08% relative error.
MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression
Venue: arXiv cs.LG 2025
Year: 2025

Deploying transformer-based neural networks on resource-constrained edge devices presents a significant challenge. This challenge is often addressed through various techniques, such as low-rank approximation and mixed-precision quantization. In this work, we introduce Mixed Low-Rank and Quantization (MLoRQ), a novel method that integrates both techniques. MLoRQ employs a two-stage optimization process to determine optimal bit-width and rank assignments for each layer, adhering to predefined memory constraints. This process includes: (i) an intra-layer optimization that identifies potentially optimal compression solutions out of all low-rank and quantization combinations; (ii) an inter-layer optimization that assigns bit-width precision and rank to each layer while ensuring the memory constraint is met. An optional final step applies a sequential optimization process using a modified adaptive rounding technique to mitigate compression-induced errors in joint low-rank approximation and quantization. The method is compatible and can be seamlessly integrated with most existing quantization algorithms. MLoRQ shows state-of-the-art results with up to 15\% performance improvement, evaluated on Vision Transformers for image classification, object detection, and instance segmentation tasks.
Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection
Venue: arXiv cs.CV 2025
Year: 2025

Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples significantly limits the effectiveness of existing methods in tasks such as localization and classification. While several anomaly synthesis approaches have been introduced for data augmentation, they often struggle with low realism, inaccurate mask alignment, and poor generalization. To overcome these limitations, we propose Generate Aligned Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework. GAA leverages the strong priors of a pretrained latent diffusion model to generate realistic, diverse, and semantically aligned anomalies using only a small number of samples. The framework first employs Localized Concept Decomposition to jointly model the semantic features and spatial information of anomalies, enabling flexible control over the type and location of anomalies. It then utilizes Adaptive Multi-Round Anomaly Clustering to perform fine-grained semantic clustering of anomaly concepts, thereby enhancing the consistency of anomaly representations. Subsequently, a region-guided mask generation strategy ensures precise alignment between anomalies and their corresponding masks, while a low-quality sample filtering module is introduced to further improve the overall quality of the generated samples. Extensive experiments on the MVTec AD and LOCO datasets demonstrate that GAA achieves superior performance in both anomaly synthesis quality and downstream tasks such as localization and classification.
Disentanglement and Assessment of Shortcuts in Ophthalmological Retinal Imaging Exams
Venue: arXiv cs.CV 2025
Year: 2025

Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. While screening reduces the risk of blindness, traditional imaging is often costly and inaccessible. Artificial intelligence (AI) algorithms present a scalable diagnostic solution, but concerns regarding fairness and generalization persist. This work evaluates the fairness and performance of image-trained models in DR prediction, as well as the impact of disentanglement as a bias mitigation technique, using the diverse mBRSET fundus dataset. Three models, ConvNeXt V2, DINOv2, and Swin V2, were trained on macula images to predict DR and sensitive attributes (SAs) (e.g., age and gender/sex). Fairness was assessed between subgroups of SAs, and disentanglement was applied to reduce bias. All models achieved high DR prediction performance in diagnosing (up to 94% AUROC) and could reasonably predict age and gender/sex (91% and 77% AUROC, respectively). Fairness assessment suggests disparities, such as a 10% AUROC gap between age groups in DINOv2. Disentangling SAs from DR prediction had varying results, depending on the model selected. Disentanglement improved DINOv2 performance (2% AUROC gain), but led to performance drops in ConvNeXt V2 and Swin V2 (7% and 3%, respectively). These findings highlight the complexity of disentangling fine-grained features in fundus imaging and emphasize the importance of fairness in medical imaging AI to ensure equitable and reliable healthcare solutions.
Negotiating Comfort: Simulating Personality-Driven LLM Agents in Shared Residential Social Networks
Venue: arXiv cs.SI 2025
Year: 2025

We use generative agents powered by large language models (LLMs) to simulate a social network in a shared residential building, driving the temperature decisions for a central heating system. Agents, divided into Family Members and Representatives, consider personal preferences, personal traits, connections, and weather conditions. Daily simulations involve family-level consensus followed by building-wide decisions among representatives. We tested three personality traits distributions (positive, mixed, and negative) and found that positive traits correlate with higher happiness and stronger friendships. Temperature preferences, assertiveness, and selflessness have a significant impact on happiness and decisions. This work demonstrates how LLM-driven agents can help simulate nuanced human behavior where complex real-life human simulations are difficult to set.
Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
Venue: arXiv cs.AI 2025
Year: 2025

Large reasoning models (LRMs) like OpenAI o1 and DeepSeek R1 have demonstrated impressive performance on complex reasoning tasks like mathematics and programming with long Chain-of-Thought (CoT) reasoning sequences (slow-thinking), compared with traditional large language models (fast-thinking). However, these reasoning models also face a huge challenge that generating unnecessarily lengthy and redundant reasoning chains even for trivial questions. This phenomenon leads to a significant waste of inference resources, increases the response time for simple queries, and hinders the practical application of LRMs in real-world products. To this end, it is crucial to shorten lengthy reasoning chains and learn adaptive reasoning between fast and slow thinking based on input difficulty. In this survey, we provide a comprehensive overview of recent progress in concise and adaptive thinking for efficient reasoning of LRMs, including methodologies, benchmarks, and challenges for future exploration. We hope this survey can help researchers quickly understand the landscape of this field and inspire novel adaptive thinking ideas to facilitate better usage of LRMs.
What Matters Most? A Quantitative Meta-Analysis of AI-Based Predictors for Startup Success
Venue: arXiv cs.CE 2025
Year: 2025

Background: Predicting startup success with machine learning is a rapidly growing field, yet findings on key predictors are often fragmented and context-specific. This makes it difficult to discern robust patterns and highlights a need for a systematic synthesis of the evidence. Methods: This study conducts a quantitative meta-analysis to synthesize the literature on predictor importance in AI-based startup evaluation. We performed a systematic review to identify a final sample of 13 empirical studies that report rankable feature importance. From these papers, we extracted and categorized 58 unique predictors, synthesizing their importance using a Weighted Importance Score (WIS) that balances a feature's average rank with its frequency of appearance. We also conducted a moderator analysis to investigate how predictor importance changes with context (e.g., success definition). Results: Our aggregate analysis reveals that the most consistently powerful predictors are a quartet of foundational attributes: Firm Characteristics (e.g., age, location), Investor Structure (e.g., investor quality), Digital and Social Traction (e.g., online momentum), and Funding History. The moderator analysis further reveals that this hierarchy is highly context-dependent. For instance, predicting near-term funding milestones elevates the importance of the deal's immediate context, while predicting long-term exits prioritizes fundamental firm and investor characteristics. Conclusion: The factors that best predict startup success are not universal but are contingent on the startup's goals, stage, and the data used for evaluation. Our findings point to a potential "convenience bias" in the literature, where predictor importance may be tied to data accessibility. We conclude by underscoring the need for standardized reporting practices to enable more robust, cumulative knowledge building in the field.
Prompt2DEM: High-Resolution DEMs for Urban and Open Environments from Global Prompts Using a Monocular Foundation Model
Venue: arXiv cs.CV 2025
Year: 2025

High-resolution elevation estimations are essential to understand catchment and hillslope hydrology, study urban morphology and dynamics, and monitor the growth, decline, and mortality of terrestrial ecosystems. Various deep learning approaches (e.g., super-resolution techniques, monocular depth estimation) have been developed to create high-resolution Digital Elevation Models (DEMs). However, super-resolution techniques are limited by the upscaling factor, and monocular depth estimation lacks global elevation context, making its conversion to a seamless DEM restricted. The recently introduced technique of prompt-based monocular depth estimation has opened new opportunities to extract estimates of absolute elevation in a global context. We present here a framework for the estimation of high-resolution DEMs as a new paradigm for absolute global elevation mapping. It is exemplified using low-resolution Shuttle Radar Topography Mission (SRTM) elevation data as prompts and high-resolution RGB imagery from the National Agriculture Imagery Program (NAIP). The approach fine-tunes a vision transformer encoder with LiDAR-derived DEMs and employs a versatile prompting strategy, enabling tasks such as DEM estimation, void filling, and updating. Our framework achieves a 100x resolution gain (from 30-m to 30-cm), surpassing prior methods by an order of magnitude. Evaluations across three diverse U.S. landscapes show robust generalization, capturing urban structures and fine-scale terrain features with < 5 m MAE relative to LiDAR, improving over SRTM by up to 18%. Hydrological analysis confirms suitability for hazard and environmental studies. We demonstrate scalability by applying the framework to large regions in the U.S. and Israel. All code and pretrained models are publicly available at: https://osherr1996.github.io/prompt2dem_propage/.
EPT-2 Technical Report
Venue: arXiv cs.LG 2025
Year: 2025

We present EPT-2, the latest iteration in our Earth Physics Transformer (EPT) family of foundation AI models for Earth system forecasting. EPT-2 delivers substantial improvements over its predecessor, EPT-1.5, and sets a new state of the art in predicting energy-relevant variables-including 10m and 100m wind speed, 2m temperature, and surface solar radiation-across the full 0-240h forecast horizon. It consistently outperforms leading AI weather models such as Microsoft Aurora, as well as the operational numerical forecast system IFS HRES from the European Centre for Medium-Range Weather Forecasts (ECMWF). In parallel, we introduce a perturbation-based ensemble model of EPT-2 for probabilistic forecasting, called EPT-2e. Remarkably, EPT-2e significantly surpasses the ECMWF ENS mean-long considered the gold standard for medium- to longrange forecasting-while operating at a fraction of the computational cost. EPT models, as well as third-party forecasts, are accessible via the app.jua.ai platform.
IteraOptiRacing: A Unified Planning-Control Framework for Real-time Autonomous Racing for Iterative Optimal Performance
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a unified planning-control strategy for competing with other racing cars called IteraOptiRacing in autonomous racing environments. This unified strategy is proposed based on Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which can improve lap time performance in the presence of surrounding racing obstacles. By iteratively using the ego car's historical data, both obstacle avoidance for multiple moving cars and time cost optimization are considered in this unified strategy, resulting in collision-free and time-optimal generated trajectories. The algorithm's constant low computation burden and suitability for parallel computing enable real-time operation in competitive racing scenarios. To validate its performance, simulations in a high-fidelity simulator are conducted with multiple randomly generated dynamic agents on the track. Results show that the proposed strategy outperforms existing methods across all randomly generated autonomous racing scenarios, enabling enhanced maneuvering for the ego racing car.
Improved Directed Expander Decompositions
Venue: arXiv cs.DS 2025
Year: 2025

We obtain faster expander decomposition algorithms for directed graphs, matching the guarantees of Saranurak and Wang (SODA 2019) for expander decomposition on undirected graphs. Our algorithms are faster than prior work and also generalize almost losslessly to capacitated graphs. In particular, we obtain the first directed expander decomposition algorithm for capacitated graphs in near-linear time with optimal dependence on $ϕ$. To obtain our result, we provide the first implementation and analysis of the non-stop cut-matching game for directed, capacitated graphs. All existing directed expander decomposition algorithms instead temporarily add ''fake edges'' before pruning them away in a final cleanup step. Our result shows that the natural undirected approach applies even to directed graphs. The difficulty is in its analysis, which is technical and requires significant modifications from the original setting of undirected graphs.
Efficient FRW Transitions via Stochastic Finite Differences for Handling Non-Stratified Dielectrics
Venue: arXiv cs.AR 2025
Year: 2025

The accuracy of floating-random-walk (FRW) based capacitance extraction stands only when the recursive FRW transitions are sampled unbiasedly according to surrounding dielectrics. Advanced technology profiles, featuring complicated non-stratified dielectrics, challenge the accuracy of existing FRW transition schemes that approximate dielectrics with stratified or eight-octant patterns. In this work, we propose an algorithm named MicroWalk, enabling accurate FRW transitions for arbitrary dielectrics while keeping high efficiency. It is provably unbiased and equivalent to using transition probabilities solved by finite difference method, but at orders of magnitude lower cost (802$\times$ faster). An enhanced 3-D capacitance solver is developed with a hybrid strategy for complicated dielectrics, combining MicroWalk with the special treatment for the first transition cube and the analytical algorithm for stratified cubes. Experiments on real-world structures show that our solver achieves a significant accuracy advantage over existing FRW solvers, while preserving high efficiency.
Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but they exhibit problems with logical consistency in the output they generate. How can we harness LLMs' broad-coverage parametric knowledge in formal reasoning despite their inconsistency? We present a method for directly integrating an LLM into the interpretation function of the formal semantics for a paraconsistent logic. We provide experimental evidence for the feasibility of the method by evaluating the function using datasets created from several short-form factuality benchmarks. Unlike prior work, our method offers a theoretical framework for neurosymbolic reasoning that leverages an LLM's knowledge while preserving the underlying logic's soundness and completeness properties.
Your Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding
Venue: arXiv cs.CL 2025
Year: 2025

Curriculum learning is a widely adopted training strategy in natural language processing (NLP), where models are exposed to examples organized by increasing difficulty to enhance learning efficiency and performance. However, most existing approaches rely on manually defined difficulty metrics -- such as text length -- which may not accurately reflect the model's own perspective. To overcome this limitation, we present a self-adaptive curriculum learning paradigm that prioritizes fine-tuning examples based on difficulty scores predicted by pre-trained language models (PLMs) themselves. Building on these scores, we explore various training strategies that differ in the ordering of examples for the fine-tuning: from easy-to-hard, hard-to-easy, to mixed sampling. We evaluate our method on four natural language understanding (NLU) datasets covering both binary and multi-class classification tasks. Experimental results show that our approach leads to faster convergence and improved performance compared to standard random sampling.